Related papers: Enhancing Underwater Light Field Images via Global Geometry-aware Diffusion Process

Enhancing Underwater Light Field Images via Global Geometry-aware Diffusion Process

URL: http://arxiv.org/abs/2601.21179v1
Date: Thu, 29 Jan 2026 02:27:22 GMT
Title: Enhancing Underwater Light Field Images via Global Geometry-aware Diffusion Process
Authors: Yuji Lin, Qian Zhao, Zongsheng Yue, Junhui Hou, Deyu Meng,
Abstract summary: GeoDiff-LF is a novel diffusion-based framework built upon SD-Turbo to enhance underwater 4-D LF imaging.<n>By integrating diffusion priors and LF geometry, GeoDiff-LF effectively mitigates color distortion in underwater scenes.
Score: 93.00033672476206
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: This work studies the challenging problem of acquiring high-quality underwater images via 4-D light field (LF) imaging. To this end, we propose GeoDiff-LF, a novel diffusion-based framework built upon SD-Turbo to enhance underwater 4-D LF imaging by leveraging its spatial-angular structure. GeoDiff-LF consists of three key adaptations: (1) a modified U-Net architecture with convolutional and attention adapters to model geometric cues, (2) a geometry-guided loss function using tensor decomposition and progressive weighting to regularize global structure, and (3) an optimized sampling strategy with noise prediction to improve efficiency. By integrating diffusion priors and LF geometry, GeoDiff-LF effectively mitigates color distortion in underwater scenes. Extensive experiments demonstrate that our framework outperforms existing methods across both visual fidelity and quantitative performance, advancing the state-of-the-art in enhancing underwater imaging. The code will be publicly available at https://github.com/linlos1234/GeoDiff-LF.

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